Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "81" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 32 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 32 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460015 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.503292 | 15.680189 | 0.489451 | 10.215838 | 0.600046 | 6.756419 | 0.162507 | 1.961924 | 0.5360 | 0.0389 | 0.4111 | nan | nan |
| 2460014 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.851770 | 13.275026 | 0.384302 | 7.658734 | -0.199903 | 10.224524 | 0.786331 | 1.923991 | 0.5045 | 0.0356 | 0.3915 | nan | nan |
| 2460013 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.644718 | 15.739500 | 0.508860 | 10.216340 | 1.188500 | 6.714848 | 0.045089 | 2.605932 | 0.5309 | 0.0373 | 0.4068 | nan | nan |
| 2460012 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.529352 | 14.768123 | 0.367420 | 10.017529 | 1.150051 | 7.493159 | 1.885926 | 4.987375 | 0.5223 | 0.0389 | 0.3986 | nan | nan |
| 2460011 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.822453 | 15.881623 | 0.191242 | 13.377499 | 0.591149 | 15.681560 | 0.679097 | 2.689978 | 0.5381 | 0.0409 | 0.4111 | nan | nan |
| 2460010 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.615673 | 17.244803 | 0.125535 | 11.075355 | -0.077463 | 10.254055 | 0.198781 | 2.262793 | 0.5446 | 0.0413 | 0.4219 | nan | nan |
| 2460009 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.238698 | 16.022925 | 0.245883 | 12.221970 | 0.095333 | 8.574555 | -0.289110 | 2.477533 | 0.5506 | 0.0400 | 0.4184 | nan | nan |
| 2460008 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.598001 | 19.539289 | 0.267255 | 13.450041 | -0.233422 | 7.578997 | 2.317253 | 5.633502 | 0.6023 | 0.0427 | 0.4365 | nan | nan |
| 2460007 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.555901 | 14.696190 | 0.406468 | 10.541307 | 0.439647 | 6.966058 | 0.050728 | 2.413755 | 0.5598 | 0.0406 | 0.4247 | nan | nan |
| 2459999 | digital_ok | 0.00% | 90.39% | 99.92% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1454 | 0.0315 | 0.0961 | nan | nan |
| 2459998 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.204386 | 12.443976 | 0.285159 | 8.859045 | 0.982906 | 9.779111 | 0.166165 | 2.259169 | 0.5514 | 0.0375 | 0.4384 | nan | nan |
| 2459997 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.409626 | 13.573707 | 0.238758 | 9.532718 | 0.130898 | 9.308639 | 0.183376 | 3.259286 | 0.5663 | 0.0412 | 0.4434 | nan | nan |
| 2459996 | digital_ok | 100.00% | 98.70% | 99.03% | 0.00% | - | - | 218.303917 | 218.715884 | inf | inf | 3025.619523 | 3095.403550 | 4669.576645 | 4806.633660 | 0.4811 | 0.3424 | 0.3336 | nan | nan |
| 2459995 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.286072 | 14.806748 | 0.180201 | 10.967981 | 0.426902 | 9.004647 | -0.257582 | 1.156767 | 0.5671 | 0.0444 | 0.4302 | nan | nan |
| 2459994 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.244582 | 14.384252 | 0.256828 | 9.614230 | -0.003885 | 9.230381 | -0.431876 | 0.789519 | 0.5648 | 0.0387 | 0.4284 | nan | nan |
| 2459993 | digital_ok | 100.00% | 66.43% | 99.64% | 0.00% | - | - | -0.246750 | 13.575572 | 0.017673 | 8.857428 | -0.119309 | 10.529237 | 0.116705 | 2.384512 | 0.2499 | 0.0329 | 0.1781 | nan | nan |
| 2459991 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.486386 | 16.783900 | 0.228392 | 9.412204 | -0.587739 | 10.408492 | 0.158463 | 1.208758 | 0.5765 | 0.0375 | 0.4466 | nan | nan |
| 2459990 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.386910 | 13.831576 | 0.278764 | 9.122145 | -0.169321 | 10.696867 | 0.176663 | 1.100847 | 0.5737 | 0.0397 | 0.4362 | nan | nan |
| 2459989 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.397310 | 14.010480 | 0.250129 | 8.364817 | -0.255221 | 8.943891 | -0.278845 | 0.457957 | 0.5688 | 0.0355 | 0.4337 | nan | nan |
| 2459988 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.250437 | 16.396517 | 0.227815 | 9.356212 | -0.586529 | 12.837055 | -0.029679 | 0.737667 | 0.5689 | 0.0366 | 0.4428 | nan | nan |
| 2459987 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.314331 | 13.702395 | 0.125890 | 9.267065 | 0.104397 | 7.736835 | 0.604786 | 2.929452 | 0.5785 | 0.0398 | 0.4412 | nan | nan |
| 2459986 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.240500 | 16.800340 | 0.179803 | 9.989242 | -0.114820 | 10.885006 | -0.594235 | 9.537142 | 0.6044 | 0.0373 | 0.4578 | nan | nan |
| 2459985 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.005875 | 15.221719 | 0.100532 | 9.310852 | -0.230939 | 8.323926 | -0.226979 | 2.075559 | 0.5782 | 0.0371 | 0.4496 | nan | nan |
| 2459984 | digital_ok | 100.00% | 0.00% | 99.35% | 0.00% | - | - | -0.230132 | 14.598592 | 0.097942 | 9.647678 | 0.514212 | 12.190992 | 3.243135 | 6.173817 | 0.5914 | 0.0418 | 0.4545 | nan | nan |
| 2459983 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.454147 | 14.340403 | 0.217804 | 9.117403 | -0.643022 | 10.828914 | -0.416295 | 6.192743 | 0.6038 | 0.0389 | 0.4310 | nan | nan |
| 2459982 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.941994 | 11.261127 | 0.452540 | 7.837672 | 0.093747 | 5.072028 | 0.271204 | 3.253273 | 0.6660 | 0.0372 | 0.4854 | nan | nan |
| 2459981 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.237892 | 13.210799 | 0.160021 | 9.676334 | -0.544498 | 11.957970 | 0.586839 | 0.884346 | 0.5776 | 0.0396 | 0.4479 | nan | nan |
| 2459980 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.352816 | 12.695092 | 0.039046 | 8.855309 | -0.332630 | 10.426313 | 0.387434 | 5.515351 | 0.6281 | 0.0390 | 0.4655 | nan | nan |
| 2459979 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.336781 | 13.318875 | -0.033308 | 8.272299 | -0.222764 | 9.801176 | 0.550303 | 1.721816 | 0.5697 | 0.0369 | 0.4410 | nan | nan |
| 2459978 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.406053 | 13.559178 | -0.001239 | 8.901021 | -0.239531 | 10.611417 | 1.263017 | 2.329377 | 0.5699 | 0.0350 | 0.4491 | nan | nan |
| 2459977 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.205494 | 14.280091 | 0.012849 | 8.809798 | -0.436696 | 10.973370 | 0.023566 | 1.469789 | 0.5316 | 0.0404 | 0.4072 | nan | nan |
| 2459976 | digital_ok | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.254713 | 13.760830 | 0.064983 | 9.146913 | -0.109743 | 10.403791 | -0.207620 | 1.225289 | 0.5775 | 0.0363 | 0.4496 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 15.680189 | 15.680189 | -0.503292 | 10.215838 | 0.489451 | 6.756419 | 0.600046 | 1.961924 | 0.162507 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.275026 | -0.851770 | 13.275026 | 0.384302 | 7.658734 | -0.199903 | 10.224524 | 0.786331 | 1.923991 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 15.739500 | -0.644718 | 15.739500 | 0.508860 | 10.216340 | 1.188500 | 6.714848 | 0.045089 | 2.605932 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.768123 | -0.529352 | 14.768123 | 0.367420 | 10.017529 | 1.150051 | 7.493159 | 1.885926 | 4.987375 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 15.881623 | -0.822453 | 15.881623 | 0.191242 | 13.377499 | 0.591149 | 15.681560 | 0.679097 | 2.689978 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 17.244803 | -0.615673 | 17.244803 | 0.125535 | 11.075355 | -0.077463 | 10.254055 | 0.198781 | 2.262793 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 16.022925 | -0.238698 | 16.022925 | 0.245883 | 12.221970 | 0.095333 | 8.574555 | -0.289110 | 2.477533 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 19.539289 | 19.539289 | -0.598001 | 13.450041 | 0.267255 | 7.578997 | -0.233422 | 5.633502 | 2.317253 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.696190 | -0.555901 | 14.696190 | 0.406468 | 10.541307 | 0.439647 | 6.966058 | 0.050728 | 2.413755 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 12.443976 | -0.204386 | 12.443976 | 0.285159 | 8.859045 | 0.982906 | 9.779111 | 0.166165 | 2.259169 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.573707 | -0.409626 | 13.573707 | 0.238758 | 9.532718 | 0.130898 | 9.308639 | 0.183376 | 3.259286 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | ee Power | inf | 218.303917 | 218.715884 | inf | inf | 3025.619523 | 3095.403550 | 4669.576645 | 4806.633660 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.806748 | -0.286072 | 14.806748 | 0.180201 | 10.967981 | 0.426902 | 9.004647 | -0.257582 | 1.156767 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.384252 | -0.244582 | 14.384252 | 0.256828 | 9.614230 | -0.003885 | 9.230381 | -0.431876 | 0.789519 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.575572 | -0.246750 | 13.575572 | 0.017673 | 8.857428 | -0.119309 | 10.529237 | 0.116705 | 2.384512 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 16.783900 | -0.486386 | 16.783900 | 0.228392 | 9.412204 | -0.587739 | 10.408492 | 0.158463 | 1.208758 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.831576 | 13.831576 | -0.386910 | 9.122145 | 0.278764 | 10.696867 | -0.169321 | 1.100847 | 0.176663 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.010480 | 14.010480 | -0.397310 | 8.364817 | 0.250129 | 8.943891 | -0.255221 | 0.457957 | -0.278845 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 16.396517 | 16.396517 | -0.250437 | 9.356212 | 0.227815 | 12.837055 | -0.586529 | 0.737667 | -0.029679 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.702395 | -0.314331 | 13.702395 | 0.125890 | 9.267065 | 0.104397 | 7.736835 | 0.604786 | 2.929452 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 16.800340 | 16.800340 | -0.240500 | 9.989242 | 0.179803 | 10.885006 | -0.114820 | 9.537142 | -0.594235 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 15.221719 | 15.221719 | -0.005875 | 9.310852 | 0.100532 | 8.323926 | -0.230939 | 2.075559 | -0.226979 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.598592 | -0.230132 | 14.598592 | 0.097942 | 9.647678 | 0.514212 | 12.190992 | 3.243135 | 6.173817 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.340403 | -0.454147 | 14.340403 | 0.217804 | 9.117403 | -0.643022 | 10.828914 | -0.416295 | 6.192743 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 11.261127 | -0.941994 | 11.261127 | 0.452540 | 7.837672 | 0.093747 | 5.072028 | 0.271204 | 3.253273 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.210799 | 13.210799 | -0.237892 | 9.676334 | 0.160021 | 11.957970 | -0.544498 | 0.884346 | 0.586839 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 12.695092 | 12.695092 | -0.352816 | 8.855309 | 0.039046 | 10.426313 | -0.332630 | 5.515351 | 0.387434 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.318875 | -0.336781 | 13.318875 | -0.033308 | 8.272299 | -0.222764 | 9.801176 | 0.550303 | 1.721816 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.559178 | 13.559178 | -0.406053 | 8.901021 | -0.001239 | 10.611417 | -0.239531 | 2.329377 | 1.263017 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 14.280091 | -0.205494 | 14.280091 | 0.012849 | 8.809798 | -0.436696 | 10.973370 | 0.023566 | 1.469789 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 81 | N07 | digital_ok | nn Shape | 13.760830 | 13.760830 | -0.254713 | 9.146913 | 0.064983 | 10.403791 | -0.109743 | 1.225289 | -0.207620 |